A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
This report has been generated by the bigbio/quantms analysis pipeline. For information about how to interpret these results, please see the documentation.
Report
generated on 2025-09-13, 05:30 UTC
based on data in:
/home/runner/work/pmultiqc/pmultiqc/data
pmultiqc
pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant, and DIA-NN.URL: https://github.com/bigbio/pmultiqc
Experimental Design and Metadata
Experimental Design
This table shows the design of the experiment. I.e., which files and channels correspond to which sample/condition/fraction.
You can see details about it in
https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html
Showing 2/2 rows and 7/7 columns.
| Sample Name | MSstats Condition: CT | MSstats Condition: CN | MSstats Condition: QY | MSstats BioReplicate | Fraction Group | Fraction | Label |
|---|---|---|---|---|---|---|---|
| 1 | Mixture | UPS1 | 0.1 fmol | 1 | |||
| 1 | 1 | 1 | |||||
| 2 | 1 | 1 | |||||
| 2 | Mixture | UPS1 | 0.25 fmol | 2 | |||
| 3 | 1 | 1 | |||||
| 4 | 1 | 1 |
Results Overview
Summary Table
This table shows the quantms pipeline summary statistics.
This table shows the quantms pipeline summary statistics.
Showing 1/1 rows.
| #Peptides Quantified | #Proteins Quantified |
|---|---|
| 5682 | 1540 |
HeatMap
This heatmap provides an overview of the performance of the quantms DIA (DIA-NN) results.
This plot shows the pipeline performance overview. Some metrics are calculated.
*Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants;
1 = no contaminants
*Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold,
i.e. reaching 2^21 of 2^23 gives score 0.25.
*Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative
Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at
tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?),
in-source fragmentation, missed cleavages and buffer irregularities can cause a shift
(see Bittremieux 2017, DOI: 10.1002/mas.21544)
*Heatmap score[RT Alignment]: Compute 1 minus the mean absolute difference between 'RT' and 'Predicted.RT',
and take the maximum of this value and 0. 1: |RT - Predicted.RT| = 0
*Heatmap score [ID rate over RT]: Judge column occupancy over retention time.
Ideally, the LC gradient is chosen such that the number of identifications
(here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles.
Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient
optimization.Scored using 'Uniform' scoring function. i.e. constant receives good score, extreme shapes are bad
*Heatmap score [Norm Factor]: Computes the mean absolute deviation (MAD) of 'Normalisation.Factor' from its mean.
0 = high variability in normalization factors; 1 = perfectly consistent normalization factors
*Heatmap score [Peak Width]: Average peak width (RT.Stop - RT.Start). 1 = peak width equals 0;
0 = peak width equals 1 or greater
Created with MultiQC
Pipeline Result Statistics
This plot shows the quantms pipeline final result.
Including Sample Name, Possible Study Variables, identified the number of peptide in the pipeline,
and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained
from the out_msstats file. You can also remove the decoy with the `remove_decoy` parameter.
Showing 2/2 rows and 8/8 columns.
| Sample Name | MSstats Condition: CT | MSstats Condition: CN | MSstats Condition: QY | Fraction | #Peptide IDs | #Unambiguous Peptide IDs | #Modified Peptide IDs | #Protein (group) IDs |
|---|---|---|---|---|---|---|---|---|
| 1 | Mixture | UPS1 | 0.1 fmol | |||||
| 1 | 5295 | 5295 | 835 | 1477 | ||||
| 1 | 5362 | 5362 | 858 | 1494 | ||||
| 2 | Mixture | UPS1 | 0.25 fmol | |||||
| 1 | 5520 | 5520 | 881 | 1510 | ||||
| 1 | 5506 | 5506 | 879 | 1508 |
Identification Summary
Number of Peptides identified Per Protein
This plot shows the number of peptides per protein in quantms pipeline final result
This statistic is extracted from the out_msstats file. Proteins supported by more peptide
identifications can constitute more confident results.
ProteinGroups Count
Number of protein groups per raw file.
Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
Peptide ID Count
Number of unique (i.e. not counted twice) peptide sequences including modifications per Raw file.
Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
Modifications Per Raw File
Compute an occurrence table of modifications (e.g. Oxidation (M)) for all peptides, including the unmodified (but without contaminants).
Post-translational modifications contained within the identified peptide sequence.
1. _M(Oxidation (M))LVLDEADEM(Oxidation (M))LNK_
2. _(Acetyl (Protein N-term))M(Oxidation (M))YGLLLENLSEYIK_
3. DPFIANGER
* 33% of 'Oxidation (M)'
* 33% of '2 Oxidation (M)'
* 33% of 'Unmodified'
The plot will show percentages, i.e. is normalized by the total number of peptide sequences (where different charge state counts as a separate peptide) per Raw file. The sum of frequencies may exceed 100% per Raw file, since a peptide can have multiple modifications.
E.g. given three peptides in a single Raw file1. _M(Oxidation (M))LVLDEADEM(Oxidation (M))LNK_
2. _(Acetyl (Protein N-term))M(Oxidation (M))YGLLLENLSEYIK_
3. DPFIANGER
, the following frequencies arise:
* 33% of 'Acetyl (Protein N-term)'* 33% of 'Oxidation (M)'
* 33% of '2 Oxidation (M)'
* 33% of 'Unmodified'
Thus, 33% of sequences are unmodified, implying 66% are modified at least once. If a modification, e.g. Oxidation(M), occurs multiple times in a single peptide it's listed as a separate modification (e.g. '2 Oxidation (M)' for double oxidation of a single peptide).
Quantification Analysis
Peptides Quantification Table
This plot shows the quantification information of peptides in the final result (mainly the mzTab file).
The quantification information of peptides is obtained from the MSstats input file.
The table shows the quantitative level and distribution of peptides in different study variables,
run and peptiforms. The distribution show all the intensity values in a bar plot above and below
the average intensity for all the fractions, runs and peptiforms.
* BestSearchScore: It is equal to 1 - min(Q.Value) for DIA datasets. Then it is equal to
1 - min(best_search_engine_score[1]), which is from best_search_engine_score[1] column in mzTab
peptide table for DDA datasets.
* Average Intensity: Average intensity of each peptide sequence across all conditions with NA=0 or NA ignored.
* Peptide intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of fractions,
and then mean intensity in technical replicates/biological replicates separately.
Showing 50/50 rows and 6/6 columns.
| PeptideID | Protein Name | Peptide Sequence | Best Search Score | Average Intensity | CT=Mixture;CN=UPS1;QY=0.1 fmol | CT=Mixture;CN=UPS1;QY=0.25 fmol |
|---|---|---|---|---|---|---|
| 1 | TOLA_ECOLI | AAAEADDIFGELSSGK | 1.0000 | 6.6828 | 6.6581 | 6.7062 |
| 2 | G3P2_ECOLI | AAAENIIPHTTGAAK | 0.9998 | 6.4660 | 6.4003 | 6.5230 |
| 3 | RLMH_ECOLI | AAAEQSWSLSALTLPHPLVR | 1.0000 | 7.0008 | 6.9572 | 7.0405 |
| 4 | PDXJ_ECOLI | AAAEVGAPFIEIHTGCYADAK | 0.9987 | 7.3254 | 7.3254 | 0.0000 |
| 5 | RL10_ECOLI | AAAFEGELIPASQIDR | 1.0000 | 8.8590 | 8.8179 | 8.8965 |
| 6 | YFGM_ECOLI | AAAQLQQGLADTSDENLK | 1.0000 | 7.5235 | 7.5322 | 7.5147 |
| 7 | YFGM_ECOLI | AAAQLQQGLADTSDENLKAVINLR | 1.0000 | 7.3320 | 7.1963 | 7.4352 |
| 8 | RHLE_ECOLI | AAATGEALSLVCVDEHK | 0.9998 | 6.6031 | 6.5730 | 6.6313 |
| 9 | SYP_ECOLI | AAATQEMTLVDTPNAK | 1.0000 | 7.1920 | 7.1199 | 7.2538 |
| 10 | EUTL_ECOLI | AACNAFTDAVLEIAR | 1.0000 | 6.6047 | 6.5527 | 6.6511 |
| 11 | ACRB_ECOLI | AADGQMVPFSAFSSSR | 0.9998 | 6.5407 | 6.3578 | 6.6690 |
| 12 | YIDA_ECOLI | AADGSTVAQTALSYDDYR | 0.9987 | 6.6627 | 6.7245 | 6.6282 |
| 13 | ADHE_ECOLI | AADIVLQAAIAAGAPK | 1.0000 | 8.3796 | 8.3536 | 8.4041 |
| 14 | NARG_ECOLI | AADLVDALGQENNPEWK | 1.0000 | 6.7428 | 6.6793 | 6.7981 |
| 15 | OXYR_ECOLI | AADSCHVSQPTLSGQIR | 1.0000 | 7.0733 | 7.0424 | 7.1021 |
| 16 | TALA_ECOLI | AAEELEKEGINCNLTLLFSFAQAR | 1.0000 | 7.0421 | 6.9818 | 7.0951 |
| 17 | HEMY_ECOLI | AAELAGNDTIPVEITR | 1.0000 | 7.1336 | 7.1077 | 7.1581 |
| 18 | SYL_ECOLI | AAENNPELAAFIDECR | 1.0000 | 7.5469 | 7.5049 | 7.5852 |
| 19 | TALB_ECOLI | AAEQLEKEGINCNLTLLFSFAQAR | 1.0000 | 7.7539 | 7.6773 | 7.8190 |
| 20 | MBHM_ECOLI | AAESALNIDVPVNAQYIR | 1.0000 | 6.7830 | 6.7696 | 6.7960 |
| 21 | DNAG_ECOLI | AAESGVSRPVPQLKR | 0.9993 | 5.5067 | 5.3646 | 5.6135 |
| 22 | HDFR_ECOLI | AAESLYLTQSAVSFR | 1.0000 | 6.4793 | 6.4830 | 6.4756 |
| 23 | RNE_ECOLI | AAESRPAPFLIHQESNVIVR | 1.0000 | 7.4295 | 7.4118 | 7.4465 |
| 24 | HFLK_ECOLI | AAFDDAIAARENEQQYIR | 1.0000 | 6.5652 | 6.4285 | 6.6691 |
| 25 | AROF_ECOLI | AAFPLSLQQEAQIADSR | 1.0000 | 6.4807 | 6.3720 | 6.5676 |
| 26 | AROF_ECOLI | AAFPLSLQQEAQIADSRK | 0.9993 | 7.0514 | 7.0599 | 7.0428 |
| 27 | BOLA_ECOLI | AAFQPVFLEVVDESYR | 1.0000 | 7.1941 | 7.1723 | 7.2149 |
| 28 | CLPB_ECOLI | AAGATTANITQAIEQMR | 1.0000 | 7.5987 | 7.6781 | 7.5527 |
| 29 | YBIS_ECOLI | AAGEPLPAVVPAGPDNPMGLYALYIGR | 1.0000 | 6.6548 | 6.5405 | 6.7453 |
| 30 | SDHA_ECOLI | AAGLHLQESIAEQGALR | 0.9998 | 6.0757 | 6.0128 | 6.1307 |
| 31 | TALA_ECOLI | AAGLSQYEHLIDDAIAWGK | 0.9992 | 6.8263 | 6.7789 | 6.8482 |
| 32 | TALA_ECOLI | AAGLSQYEHLIDDAIAWGKK | 1.0000 | 6.9728 | 6.9756 | 6.9699 |
| 33 | ADHE_ECOLI | AAGVETEVFFEVEADPTLSIVR | 1.0000 | 6.9087 | 6.9472 | 6.8666 |
| 34 | ADHE_ECOLI | AAGVETEVFFEVEADPTLSIVRK | 0.9998 | 7.4544 | 7.3854 | 7.5139 |
| 35 | ENO_ECOLI | AAGYELGKDITLAMDCAASEFYK | 1.0000 | 7.8751 | 7.8493 | 7.8994 |
| 36 | YEBE_ECOLI | AAHQDEPQFGAQSTPLDER | 0.9998 | 5.8692 | 5.9181 | 5.8140 |
| 37 | RIBB_ECOLI | AAIADGAKPSDLNRPGHVFPLR | 1.0000 | 7.2375 | 7.1964 | 7.2750 |
| 38 | DEOC_ECOLI | AAIAYGADEVDVVFPYR | 1.0000 | 7.5553 | 7.4613 | 7.6326 |
| 39 | IDH_ECOLI | AAIEYAIANDRDSVTLVHK | 1.0000 | 7.8134 | 7.7834 | 7.8416 |
| 40 | SYFA_ECOLI | AAISQASDVAALDNVR | 1.0000 | 7.2678 | 7.2633 | 7.2721 |
| 41 | SYFA_ECOLI | AAISQASDVAALDNVRVEYLGK | 1.0000 | 7.6984 | 7.6417 | 7.7486 |
| 42 | MUKF_ECOLI | AAISSCELLLSETSGTLR | 0.9993 | 6.1472 | 6.1559 | 6.1383 |
| 43 | MSCM_ECOLI | AAKPAQPEVVEALQSALNALEER | 0.9998 | 6.0540 | 5.9369 | 6.1461 |
| 44 | AMPN_ECOLI | AALEQLKGLENLSGDLYEK | 1.0000 | 7.5103 | 7.4162 | 7.5875 |
| 45 | DBHA_ECOLI | AALESTLAAITESLK | 1.0000 | 7.9537 | 7.9562 | 7.9512 |
| 46 | HEM3_ECOLI | AALPPEISLPAVGQGAVGIECR | 0.9993 | 6.4280 | 6.3064 | 6.5230 |
| 47 | SDHA_ECOLI | AALQISQSGQTCALLSK | 1.0000 | 6.1566 | 6.0976 | 6.2086 |
| 48 | THIP_ECOLI | AAMLALLQMVCCLGLVLLSQR | 0.9929 | 5.8504 | 5.8120 | 5.8857 |
| 49 | DHE4_ECOLI | AANAGGVATSGLEMAQNAAR | 1.0000 | 6.8077 | 6.7297 | 6.8737 |
| 50 | GRCA_ECOLI | AANDDLLNSFWLLDSEK | 0.9998 | 6.5810 | 6.5594 | 6.6016 |
Protein Quantification Table
This plot shows the quantification information of proteins in the final result (mainly the mzTab file).
The quantification information of proteins is obtained from the msstats input file.
The table shows the quantitative level and distribution of proteins in different study variables and run.
* Peptides_Number: The number of peptides for each protein.
* Average Intensity: Average intensity of each protein across all conditions with NA=0 or NA ignored.
* Protein intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of peptides.
Showing 50/50 rows and 5/5 columns.
| ProteinID | Protein Name | Number of Peptides | Average Intensity | CT=Mixture;CN=UPS1;QY=0.1 fmol | CT=Mixture;CN=UPS1;QY=0.25 fmol |
|---|---|---|---|---|---|
| 1 | 3PASE_ECOLI | 1 | 6.9494 | 6.8807 | 7.0088 |
| 2 | 5DNU_ECOLI | 1 | 6.1932 | 6.1003 | 6.2696 |
| 3 | 6PGD_ECOLI | 10 | 8.3638 | 8.3396 | 8.3867 |
| 4 | 6PGL_ECOLI | 2 | 7.6326 | 7.5889 | 7.6640 |
| 5 | AAS_ECOLI | 3 | 6.8951 | 6.8911 | 6.9008 |
| 6 | AAT_ECOLI | 2 | 7.9658 | 7.9296 | 7.9991 |
| 7 | ABGT_ECOLI | 1 | 7.7790 | 7.7790 | 0.0000 |
| 8 | ACCA_ECOLI | 11 | 8.2755 | 8.2307 | 8.3152 |
| 9 | ACCC_ECOLI | 12 | 8.4620 | 8.4276 | 8.4927 |
| 10 | ACCD_ECOLI | 4 | 7.8815 | 7.8808 | 7.8823 |
| 11 | ACEA_ECOLI | 10 | 7.7598 | 7.6947 | 7.7891 |
| 12 | ACFD_ECOLI | 8 | 7.6336 | 7.6026 | 7.6641 |
| 13 | ACKA_ECOLI | 11 | 9.1093 | 9.0727 | 9.1428 |
| 14 | ACNA_ECOLI | 3 | 6.7833 | 6.6970 | 6.8553 |
| 15 | ACNB_ECOLI | 12 | 8.2805 | 8.2312 | 8.3191 |
| 16 | ACP_ECOLI | 1 | 7.6389 | 7.5918 | 7.6815 |
| 17 | ACRA_ECOLI | 6 | 8.1738 | 8.1263 | 8.2167 |
| 18 | ACRB_ECOLI | 6 | 8.3756 | 8.3427 | 8.4068 |
| 19 | ACSA_ECOLI | 1 | 6.1529 | 6.0606 | 6.1926 |
| 20 | ACUI_ECOLI | 4 | 7.8570 | 7.7718 | 7.9282 |
| 21 | ACYP_ECOLI | 1 | 6.3378 | 6.3316 | 6.3409 |
| 22 | ADD_ECOLI | 6 | 8.0337 | 8.0400 | 8.0307 |
| 23 | ADEC_ECOLI | 1 | 5.4576 | 5.4344 | 5.4797 |
| 24 | ADHE_ECOLI | 33 | 9.5549 | 9.5068 | 9.5983 |
| 25 | ADHP_ECOLI | 2 | 6.5932 | 6.6095 | 6.5762 |
| 26 | ADIA_ECOLI | 3 | 7.1124 | 7.0835 | 7.1395 |
| 27 | ADPP_ECOLI | 2 | 7.0229 | 6.9876 | 7.0556 |
| 28 | AGP_ECOLI | 3 | 6.6330 | 6.5797 | 6.6804 |
| 29 | AHPC_ECOLI | 10 | 9.0158 | 9.0115 | 9.0242 |
| 30 | AHPF_ECOLI | 10 | 8.3830 | 8.3442 | 8.4196 |
| 31 | AK1H_ECOLI | 7 | 7.3497 | 7.3095 | 7.3904 |
| 32 | AK2H_ECOLI | 4 | 7.1237 | 7.0812 | 7.1625 |
| 33 | AK3_ECOLI | 6 | 7.4464 | 7.2458 | 7.4599 |
| 34 | ALAA_ECOLI | 2 | 6.8790 | 6.8346 | 6.9194 |
| 35 | ALAC_ECOLI | 5 | 8.0506 | 8.0097 | 8.0880 |
| 36 | ALDB_ECOLI | 1 | 6.4192 | 6.4015 | 6.4362 |
| 37 | ALF1_ECOLI | 3 | 7.1007 | 6.8972 | 7.0926 |
| 38 | ALF_ECOLI | 12 | 9.4015 | 9.3588 | 9.4395 |
| 39 | ALKH_ECOLI | 4 | 7.7365 | 7.6386 | 7.8109 |
| 40 | ALLE_ECOLI | 1 | 6.2522 | 6.1514 | 6.3340 |
| 41 | ALR1_ECOLI | 3 | 7.3731 | 7.3394 | 7.4043 |
| 42 | AMIA_ECOLI | 1 | 5.9071 | 5.8856 | 5.9276 |
| 43 | AMIB_ECOLI | 2 | 6.4927 | 6.4137 | 6.5595 |
| 44 | AMIC_ECOLI | 4 | 6.9237 | 6.8522 | 6.9852 |
| 45 | AMN_ECOLI | 6 | 7.3552 | 7.3334 | 7.3729 |
| 46 | AMPA_ECOLI | 7 | 7.8893 | 7.8640 | 7.9138 |
| 47 | AMPC_ECOLI | 2 | 7.1425 | 7.1013 | 7.1801 |
| 48 | AMPH_ECOLI | 2 | 6.5330 | 6.4786 | 6.5813 |
| 49 | AMPN_ECOLI | 7 | 8.1028 | 8.0590 | 8.1426 |
| 50 | AMPP_ECOLI | 2 | 7.1264 | 7.0775 | 7.1703 |
Intensity Distribution
log2(Precursor.Quantity) for each Run.
[DIA-NN: main report] log2(Precursor.Quantity) for each Run.
Created with MultiQC
Standard Deviation of Intensity
Standard deviation of intensity under different experimental conditions.
[DIA-NN: report.tsv] First, identify the experimental conditions from the "Run" name.
Then, group the data by experimental condition and Modified.Sequence, and calculate
the standard deviation of log2(Precursor.Quantity).
Created with MultiQC
MS1 Analysis
Total Ion Chromatograms
MS1 quality control information extracted from the spectrum files.
This plot displays Total Ion Chromatograms (TICs) derived from MS1 scans across all analyzed samples.
The x-axis represents retention time, and the y-axis shows the total ion intensity at each time point.
Each colored trace corresponds to a different sample. The TIC provides a global view of the ion signal
throughout the LC-MS/MS run, reflecting when compounds elute from the chromatography column.
Key aspects to assess include:
* Overall intensity pattern: A consistent baseline and similar peak profiles across samples indicate good reproducibility.
* Major peak alignment: Prominent peaks appearing at similar retention times suggest stable chromatographic performance.
* Signal-to-noise ratio: High peaks relative to baseline noise reflect better sensitivity.
* Chromatographic resolution: Sharp, well-separated peaks indicate effective separation.
* Signal drift: A gradual decline in signal intensity across the run may point to source contamination or chromatography issues.
Deviations such as shifted retention times, missing peaks, or inconsistent intensities may signal problems
in sample preparation, LC conditions, or mass spectrometer performance that require further investigation.
Created with MultiQC
MS1 Base Peak Chromatograms
MS1 base peak chromatograms extracted from the spectrum files.
The Base Peak Chromatogram (BPC) displays the intensity of the most abundant ion at each retention
time point across your LC-MS run. Unlike the Total Ion Chromatogram (TIC) which shows the summed
intensity of all ions, the BPC highlights the strongest signals, providing better visualization
of compounds with high abundance while reducing baseline noise. This makes it particularly useful
for identifying major components in complex samples, monitoring dominant species, and providing
clearer peak visualization when signal-to-noise ratio is a concern. Comparing BPC patterns across
samples allows you to evaluate consistency in the detection of high-abundance compounds
and can reveal significant variations in sample composition or instrument performance.
Created with MultiQC
MS1 Peaks
MS1 Peaks from the spectrum files
This plot shows the number of peaks detected in MS1 scans over the course of each sample run.
The x-axis represents retention time (in minutes), while the y-axis displays the number of
distinct ion signals (peaks) identified in each MS1 scan. The MS1 peak count reflects spectral
complexity and provides insight into instrument performance during the LC-MS analysis.
Key aspects to consider include:
* Overall pattern: Peak counts typically increase during the elution of complex mixtures and
decrease during column washing or re-equilibration phases.
* Peak density: Higher counts suggest more complex spectra, potentially indicating a greater
number of compounds present at that time point."
* Peak Consistency across samples: Similar profiles among replicates or related samples
indicate good analytical reproducibility.
* Sudden drops: Abrupt decreases in peak count may point to transient ionization issues,
spray instability, or chromatographic disruptions.
* Baseline values: The minimum peak count observed reflects the level of background noise
or instrument sensitivity in the absence of eluting compounds.
Monitoring MS1 peak counts complements total ion chromatogram (TIC) and base peak chromatogram
(BPC) data, offering an additional layer of quality control related to signal complexity,
instrument stability, and sample composition.
Created with MultiQC
General stats for MS1 information
General stats for MS1 information extracted from the spectrum files.
This table presents general statistics for MS1 information extracted from mass spectrometry data files."
It displays MS runs with their acquisition dates and times.
For each file, the table shows two key metrics: TotalCurrent (the sum of all MS1 ion intensities throughout the run) and ScanCurrent (the sum of MS2 ion intensities).
These values provide a quick overview of the total ion signals detected during both survey scans (MS1) and fragmentation scans (MS2),
allowing for comparison of overall signal intensity across samples. Consistent TotalCurrent and ScanCurrent values across similar samples typically
indicate good reproducibility in the mass spectrometry analysis, while significant variations may suggest issues with sample preparation,
instrument performance, or ionization efficiency. The blue shading helps visualize the relative intensity differences between samples.
Showing 4/4 rows and 3/3 columns.
| File | Acquisition Date Time | log10(Total Current) | log10(Scan Current) |
|---|---|---|---|
| RD139_Narrow_UPS1_0_1fmol_inj1 | 2018-09-01 20:06:01 | 12.2519 | 12.0890 |
| RD139_Narrow_UPS1_0_1fmol_inj2 | 2018-09-02 11:02:22 | 12.2542 | 12.1204 |
| RD139_Narrow_UPS1_0_25fmol_inj1 | 2018-09-01 22:09:01 | 12.2904 | 12.1417 |
| RD139_Narrow_UPS1_0_25fmol_inj2 | 2018-09-02 13:05:24 | 12.2901 | 12.1573 |
Ms1 Area Distribution
log2(Ms1.Area) for each Run.
[DIA-NN: report.tsv] log2(Ms1.Area) for each Run. Ms1.Area: non-normalised MS1 peak area.
Created with MultiQC
MS2 and Spectral Stats
Number of Peaks per MS/MS spectrum
This chart represents a histogram containing the number of peaks per MS/MS spectrum
in a given experiment.
This chart assumes centroid data. Too few peaks can identify poor fragmentation
or a detector fault, as opposed to a large number of peaks representing very noisy spectra.
This chart is extensively dependent on the pre-processing steps performed to the spectra
(centroiding, deconvolution, peak picking approach, etc).
Peak Intensity Distribution
This is a histogram representing the ion intensity vs.
the frequency for all MS2 spectra in a whole given experiment.
It is possible to filter the information for all, identified and unidentified spectra.
This plot can give a general estimation of the noise level of the spectra.
Generally, one should expect to have a high number of low intensity noise peaks with a low number
of high intensity signal peaks.
A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or
potential experimental problems. In the case of data reuse this plot can be useful in
identifying the requirement for pre-processing of the spectra prior to any downstream analysis.
The quality of the identifications is not linked to this data as most search engines perform internal
spectrum pre-processing before matching the spectra. Thus, the spectra reported are not
necessarily pre-processed since the search engine may have applied the pre-processing step
internally. This pre-processing is not necessarily reported in the experimental metadata.
Distribution of Precursor Charges
This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.
[DIA-NN: main report] distribution of the precursor ion charges for a given whole experiment.
Precursor.Charge: the charge of the precursor.
Charge-state of Per File
The distribution of the charge-state of the precursor ion.
[DIA-NN: main report] The distribution of the charge-state of the precursor ion (Precursor.Charge).
Mass Error Trends
Delta Mass
This plot is based on the "Ms1.Apex.Mz.Delta" column from the DIA-NN main report.
[DIA-NN: main report]
Ms1.Apex.Mz.Delta: difference between observed precursor m/z and the theoretical value.
Created with MultiQC
RT Quality Control
IDs over RT
Distribution of retention time, derived from the main report.
[DIA-NN: main report] Distribution of retention time (RT) for each run.
Created with MultiQC
Normalisation Factor over RT
Distribution of Normalisation.Factor with retention time, derived from the main report.
[DIA-NN: main report] Distribution of Normalisation.Factor with retention time (RT) for each run.
RT: the retention time (RT) of the PSM in minutes. Normalisation.Factor: normalisation factor
applied to the precursor in the specific run,
i.e. normalised quantity = normalisation factor X non-normalised quantity
Created with MultiQC
FWHM over RT
Distribution of FWHM with retention time, derived from the main report.
FWHM: estimated peak width at half-maximum.
[DIA-NN: main report] Distribution of FWHM with retention time (RT) for each run.
RT: the retention time (RT) of the PSM in minutes. FWHM: estimated peak width at half-maximum;
note that the accuracy of such estimates sometimes strongly depends on the DIA cycle time and
sample injection amount, i.e. they can only be used to evaluate chromatographic performance in
direct comparisons with similar settings, including the scan window; another caveat is that
FWHM does not reflect any peak tailing.
Created with MultiQC
Peak Width over RT
Distribution of peak width with retention time, derived from the main report.
Peak Width = RT.Stop - RT.Start.
[DIA-NN: main report] Distribution of peak width with retention time (RT) for each run.
RT: the retention time (RT) of the PSM in minutes. RT.Start and RT.Stop: peak boundaries.
Created with MultiQC
Absolute RT Error over RT
Distribution of rt error with retention time, derived from the main report.
[DIA-NN: main report] Distribution of absolute RT error (|RT - Predicted.RT|) with retention time (RT) for each run.
RT: the retention time (RT) of the PSM in minutes.
Predicted.RT: predicted RT based on the iRT.
Created with MultiQC
LOESS RT ~ iRT
Distribution of LOESS RT ~ iRT for each run, derived from the main report.
[DIA-NN: main report] Distribution of LOESS RT ~ iRT for each run.
RT: the retention time (RT) of the PSM in minutes.
iRT: reference RT as recorded in the spectral library.
Created with MultiQC
Software Versions
Software Versions lists versions of software tools extracted from file contents.
| Group | Software | Version |
|---|---|---|
| ASSEMBLE_EMPIRICAL_LIBRARY | DIA-NN | 2.1.0 |
| CONVERT_RESULTS | quantms-utils | 0.0.23 |
| FINAL_QUANTIFICATION | DIA-NN | 2.1.0 |
| GENERATE_CFG | quantms-utils | 0.0.23 |
| INDIVIDUAL_ANALYSIS | DIA-NN | 2.1.0 |
| MSSTATS_LFQ | bioconductor-msstats | 4.14.0 |
| r-base | 4.4.2 | |
| MZML_STATISTICS | quantms-utils | 0.0.23 |
| PRELIMINARY_ANALYSIS | DIA-NN | 2.1.0 |
| SAMPLESHEET_CHECK | quantms-utils | 0.0.23 |
| SDRF_PARSING | sdrf-pipelines | 0.0.32 |
| THERMORAWFILEPARSER | ThermoRawFileParser | 1.3.4 |
| Workflow | Nextflow | 24.10.5 |
| bigbio/quantms | v1.5.0 |
bigbio/quantms Workflow Summary
- this information is collected when the pipeline is started.URL: https://github.com/bigbio/quantms
Input/output options
- export_decoy_psm
- true
- input
- /home/yueqx/Data_Disk/proteogenomics/quantms/test_dia/PXD026600.sdrf.tsv
- outdir
- /home/yueqx/Data_Disk/proteogenomics/quantms/results_dia
SDRF validation
- skip_factor_validation
- true
- use_ols_cache_only
- true
- validate_ontologies
- true
Protein database
- database
- /home/yueqx/Data_Disk/proteogenomics/quantms/test_dia/REF_EColi_K12_UPS1_combined.fasta
Database search
- allowed_missed_cleavages
- 1
- max_fr_mz
- 1500
- max_mods
- 2
- max_peptide_length
- 30
- max_pr_mz
- 950
- max_precursor_charge
- 3
- min_fr_mz
- 500
- min_peptide_length
- 15
- min_pr_mz
- 350
Modification localization
- luciphor_debug
- 0
PSM re-scoring (general)
- run_fdr_cutoff
- 0.10
PSM re-scoring (Percolator)
- description_correct_features
- 0
Consensus ID
- consensusid_considered_top_hits
- 0
- min_consensus_support
- 0
Isobaric analyzer
- quant_activation_method
- HCD
Protein Quantification (LFQ)
- feature_with_id_min_score
- 0.10
DIA-NN
- diann_normalize
- false
Statistical post-processing
- contrasts
- pairwise
Quality control
- enable_pmultiqc
- true
- pmultiqc_idxml_skip
- true
Institutional config options
- custom_config_base
- /home/yueqx/Data_Disk/proteogenomics/quantms/test_dia/confs
Generic options
- publish_dir_mode
- symlink
- trace_report_suffix
- 2025-07-22_10-44-58
Core Nextflow options
- configFiles
- N/A
- container
- [withLabel:diann:docker.io/library/diann-2.1.0]
- containerEngine
- docker
- launchDir
- /home/yueqx/Data_Disk/proteogenomics/quantms
- profile
- docker
- projectDir
- /home/yueqx/Data_Disk/proteogenomics/quantms/bigbio/quantms
- runName
- loquacious_wilson
- userName
- yueqx
- workDir
- /home/yueqx/Data_Disk/proteogenomics/quantms/work
bigbio/quantms Methods Description
Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/bigbio/quantms
Methods
Data was processed using bigbio/quantms v1.5.0 (doi: 10.5281/zenodo.7754148) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.
The pipeline was executed with Nextflow v24.10.5 (Di Tommaso et al., 2017) with the following command:
nextflow run bigbio/quantms -profile docker --custom_config_base /home/yueqx/Data_Disk/proteogenomics/quantms/test_dia/confs -c /home/yueqx/Data_Disk/proteogenomics/quantms/test_dia/confs/run_latest_dia.configReferences
- Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
- Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
- Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
- da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
Notes:
- The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
- You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.
